Chapter 5: Further Reading - Descriptive Statistics in Basketball

Annotated Bibliography

This curated reading list provides resources for deepening your understanding of descriptive statistics and their application to basketball analytics.

Foundational Statistics Texts

Introductory Statistics

Statistics (4th Edition) David Freedman, Robert Pisani, Roger Purves | W.W. Norton, 2007

One of the best introductory statistics textbooks ever written. The authors emphasize intuition and real-world applications over formulas. Their treatment of correlation and regression is particularly clear and will help you avoid common pitfalls.

Naked Statistics: Stripping the Dread from the Data Charles Wheelan | W.W. Norton, 2013

An accessible, entertaining introduction to statistics for general audiences. While not basketball-specific, it covers all the core concepts from this chapter with memorable examples. Good for building intuition before diving into technical details.

The Art of Statistics: How to Learn from Data David Spiegelhalter | Basic Books, 2019

Written by a renowned statistician, this book emphasizes statistical thinking and communication. Excellent for understanding how to present statistical findings to non-technical audiences like coaches or front office personnel.

Mathematical Foundations

Mathematical Statistics with Applications (7th Edition) Dennis Wackerly, William Mendenhall, Richard Scheaffer | Cengage, 2008

A more rigorous treatment of the mathematical foundations underlying descriptive statistics. Recommended for those who want to understand the theory behind the formulas.

All of Statistics: A Concise Course in Statistical Inference Larry Wasserman | Springer, 2004

Graduate-level text that covers probability and statistics comprehensively. Chapter 2 on random variables and Chapter 3 on expectations provide the theoretical foundation for understanding means, variances, and distributions.

Basketball Analytics Applications

Essential Basketball Analytics Books

Basketball on Paper Dean Oliver | Potomac Books, 2004

The foundational text in basketball analytics. Oliver's use of percentages, rates, and standardized metrics throughout demonstrates practical application of descriptive statistics. His "Four Factors" analysis shows how to distill complex data into interpretable summaries.

Sprawlball: A Visual Tour of the New Era of the NBA Kirk Goldsberry | Houghton Mifflin Harcourt, 2019

While primarily focused on visualization, Goldsberry's work demonstrates effective use of descriptive statistics to characterize players and trends. His shooting efficiency analyses show how to combine multiple metrics meaningfully.

The Midrange Theory Seth Partnow | Triumph Books, 2021

Partnow's book on modern NBA analytics discusses the statistical foundations of player evaluation. His treatment of shooting efficiency and player impact metrics builds directly on descriptive statistics concepts.

Academic Papers

A Starting Point for Analyzing Basketball Statistics Dean Oliver | Journal of Quantitative Analysis in Sports, 2004

Oliver's original academic paper introducing many concepts from "Basketball on Paper." Demonstrates rigorous application of statistical methods to basketball data.

Deconstructing the Rebound with Optical Tracking Data Maheswaran et al. | MIT Sloan Sports Analytics Conference, 2012

Example of using descriptive statistics to analyze rebounding. Shows how to characterize distributions of player positioning and timing.

RAPTOR: A Modern Player Rating System FiveThirtyEight, 2019

Technical documentation of FiveThirtyEight's player rating system. Demonstrates how z-scores and standardization are used in professional analytics systems.

Statistical Computing

Python Resources

Python for Data Analysis (3rd Edition) Wes McKinney | O'Reilly Media, 2022

Written by the creator of pandas, this book covers all the Python tools needed to compute descriptive statistics. Chapter 5 on pandas and Chapter 9 on data aggregation are particularly relevant.

Think Stats: Exploratory Data Analysis (2nd Edition) Allen B. Downey | O'Reilly Media, 2014

Teaches statistics using Python programming. Each chapter includes code examples that directly implement the concepts. Available free online at greenteapress.com/thinkstats2/.

Python Data Science Handbook Jake VanderPlas | O'Reilly Media, 2016

Comprehensive coverage of NumPy, pandas, and SciPy for computing descriptive statistics. Available free online at jakevdp.github.io/PythonDataScienceHandbook/.

SciPy and Statistical Computing

SciPy Documentation: Statistical Functions https://docs.scipy.org/doc/scipy/reference/stats.html

Official documentation for scipy.stats module. Covers all statistical functions including skewness, kurtosis, percentiles, and correlation coefficients.

pandas User Guide: Computation https://pandas.pydata.org/docs/user_guide/computation.html

Official pandas documentation on statistical computations. Essential reference for mean, std, var, quantile, corr, and other descriptive methods.

NumPy Statistics Documentation https://numpy.org/doc/stable/reference/routines.statistics.html

Reference for NumPy's statistical functions. Useful for understanding the underlying implementations.

Distribution Analysis

Understanding Distributions

The Normal Distribution: A Very Short Introduction W.J. Adams | Cambridge University Press, 2020

Accessible introduction to the normal distribution and its properties. Understanding the normal distribution is essential for interpreting z-scores and statistical tests.

Fat Tails and Extremistan Nassim Nicholas Taleb | Various publications

Taleb's work on non-normal distributions and extreme events. Relevant for understanding why kurtosis matters and when normal distribution assumptions fail - important when analyzing performance outliers.

Applied Distribution Analysis

Fitting Distributions with R Vito Ricci | CRAN Documentation

While in R, the concepts translate directly to Python. Shows how to assess distribution fit and choose appropriate models.

Probability Distributions for Data Science Various online courses (Coursera, DataCamp)

Multiple courses cover probability distributions in practical contexts. Useful for understanding when different distributions apply to basketball data.

Correlation and Relationships

Correlation Analysis

Statistics for People Who (Think They) Hate Statistics (7th Edition) Neil J. Salkind | SAGE Publications, 2019

Chapters on correlation and regression are particularly accessible. Good for building intuition about what correlation measures and doesn't measure.

The Book of Why: The New Science of Cause and Effect Judea Pearl | Basic Books, 2018

Essential reading for understanding why "correlation does not imply causation." Pearl's work on causal inference helps analysts avoid common pitfalls when interpreting relationships in data.

Regression and Prediction

An Introduction to Statistical Learning (2nd Edition) Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani | Springer, 2021

Chapter 3 on linear regression builds on correlation concepts. Available free online at statlearning.com. Essential for understanding R-squared and predictive modeling.

Era-Adjusted Statistics

Historical Comparison Methods

Comparing Players Across Eras Neil Paine | FiveThirtyEight, various articles

FiveThirtyEight's approach to era-adjusted statistics demonstrates practical application of z-scores for historical comparison.

Basketball-Reference Glossary https://www.basketball-reference.com/about/glossary.html

Definitions and methods for various era-adjusted statistics. Good reference for understanding how professional sources handle standardization.

Pace Adjustment

Understanding Pace and Efficiency Basketball-Reference and NBA.com

Various resources explaining pace-adjusted statistics. Essential for understanding why raw statistics need context.

Online Resources

Tutorials and Courses

Khan Academy: Statistics and Probability https://www.khanacademy.org/math/statistics-probability

Free, comprehensive coverage of descriptive statistics with practice problems. Excellent for reviewing fundamentals.

Coursera: Statistics with Python Specialization University of Michigan

Multi-course specialization covering statistics using Python. Practical approach with coding exercises.

DataCamp: Statistical Thinking in Python Justin Bois

Interactive course on statistical analysis with Python. Covers distributions, correlation, and hypothesis testing.

Basketball Analytics Communities

APBRmetrics Forum https://apbrmetrics.com/

The original basketball analytics community. Archives contain valuable discussions of statistical methods applied to basketball.

r/nbadiscussion https://reddit.com/r/nbadiscussion

Reddit community with analytical discussions. Good source for contemporary debates about statistics and metrics.

Cleaning the Glass https://cleaningtheglass.com

Subscription analytics site with detailed statistical breakdowns. Shows professional application of descriptive statistics to player and team analysis.

Data Sources for Practice

Basketball-Reference https://www.basketball-reference.com

Comprehensive historical statistics. Use for practicing calculations and analysis on real data.

NBA Stats https://stats.nba.com

Official NBA statistics portal. More detailed current season data for practice datasets.

Kaggle NBA Datasets https://www.kaggle.com/datasets?search=nba

Various preprocessed datasets for practice. Good for learning without data collection overhead.

Video Resources

Statistics Lectures

StatQuest with Josh Starmer (YouTube) https://youtube.com/statquest

Excellent video explanations of statistical concepts. Videos on standard deviation, distributions, and correlation are particularly relevant.

3Blue1Brown (YouTube) https://youtube.com/3blue1brown

Visual explanations of mathematical concepts. Videos on probability and statistics provide deep intuition.

Basketball Analytics Presentations

MIT Sloan Sports Analytics Conference https://www.sloansportsconference.com

Annual conference with recorded presentations. Many sessions demonstrate statistical analysis of basketball data.

NESSIS (New England Symposium on Statistics in Sports) Various recordings available online

Academic sports statistics conference with rigorous methodological discussions.

Recommended Reading Order

For Beginners

1. Start with: "Naked Statistics" by Wheelan
2. Then: Khan Academy statistics course
3. Practice with: "Think Stats" by Downey
4. Apply to basketball: "Basketball on Paper" by Oliver

For Those with Statistics Background

1. Start with: "Basketball on Paper" by Oliver
2. Deepen with: "The Midrange Theory" by Partnow
3. Technical foundation: "Python for Data Analysis" by McKinney
4. Advanced: "All of Statistics" by Wasserman

For Practitioners

1. Start with: SciPy and pandas documentation
2. Read: Sloan Conference papers on player evaluation
3. Study: FiveThirtyEight methodology articles
4. Practice: Kaggle datasets and competitions

Key Takeaways from the Literature

1. Intuition matters: The best statistics texts emphasize understanding over formulas
2. Context is crucial: Basketball statistics require domain knowledge to interpret correctly
3. Visualization supports statistics: Numbers and graphs work together
4. Causation requires care: Strong correlations can be misleading
5. Era adjustment is essential: Raw statistics need context for fair comparison
6. Practice builds skill: Reading must be accompanied by hands-on analysis

Building Your Library

Essential (Start Here)

• "Statistics" by Freedman, Pisani, Purves
• "Basketball on Paper" by Oliver
• "Python for Data Analysis" by McKinney

Recommended Additions

• "The Art of Statistics" by Spiegelhalter
• "The Midrange Theory" by Partnow
• "An Introduction to Statistical Learning"

Advanced Reference

• "All of Statistics" by Wasserman
• "The Book of Why" by Pearl
• Academic papers from Sloan Conference

Last updated: Chapter 5 publication date. Check for updated resources at the textbook companion website.